Teaching Real Data Interpretation with Models (TRIM): Analysis of Student Dialogue in a Large-Enrollment Cell and...

Area of Science:

• Cell Biology Education
• Scientific Practices in Biology

Background:

• Traditional cell biology courses often lack integration of scientific practices.
• Developing students' data interpretation and model-based reasoning skills is crucial for scientific literacy.

Purpose of the Study:

• To design and evaluate a cell biology course integrating content with scientific practices.
• To understand how students interpret authentic biological data using instructor-provided models.
• To assess the development of students' data interpretation and model-based reasoning skills.

Main Methods:

• A 2-year research project embedded within a cell biology course.
• Analysis of student written work to assess data interpretation validity and generativity.
• Analysis of small-group audio recordings to observe model-based reasoning and argumentation.

Main Results:

• Students used instructor models to broaden data interpretations and link them to biological significance.
• Spontaneous argumentation occurred in 82% of transcripts, with frequent co-construction of claims and evidence.
• Collaborative decoding of data representations and pattern recognition were common strategies.
• A key challenge identified was focusing on irrelevant data patterns.

Conclusions:

• Integrating scientific practices, particularly data interpretation and model-based reasoning, is feasible in large cell biology lecture courses.
• Model-based reasoning effectively supports students in developing sophisticated data interpretations and argumentation skills.
• The course design fostered collaborative learning and enhanced students' ability to engage in authentic scientific inquiry.